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Older adults are among the fastest-growing demographic groups in the United States, increasing by over a third this past decade. Consequently, the older adult consumer prod-uct market has quickly become a multi-billion-dollar in-dustry in which millions of products are sold every year. However, the rapidly growing market raises the poten-tial for an increasing number of product safety concerns and consumer product-related injuries among older adults. Recent manufacturer and consumer injury prevention efforts have begun to turn towards online reviews, as these provide valuable information from which actionable, timely intelligence can be derived and used to detect safety concerns and prevent injury. The presented dataset contains 1966 curated online product reviews from consumers, equally distributed between safety concerns and non-concerns, pertaining to product categories typically intended for older adults. Identified safety concerns were manually sub-coded across thirteen dimensions designed to capture relevant aspects of the consumer's experience with the purchased product, facilitate the safety concern identification and sub-classification process, and serve as a gold-standard, balanced dataset for text classifier learning. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Data & Decision Sciences Center (DDS); Institute for Society, Culture, and Environment (ISCE) Scholars Institute at Virginia Tech Published version This project was supported by internal grants from the Data & Decision Sciences Center (D&DS) and the Institute for Society, Culture, and Environment (ISCE) Scholars Institute at Virginia Tech. |
